Specialized vector databases add operational complexity to your tech stack. If your application database is already on PostgreSQL, you can leverage the pgvector extension to store embeddings and perform vector searches with enterprise-grade reliability.
This guide details how to build and optimize semantic search vector spaces in PostgreSQL.
1. Setting up pgvector
Once the extension is enabled, you can define columns of type vector. The size of the vector corresponds to your embedding model's dimensions (e.g., 1536 dimensions for OpenAI's text-embedding-3-small).
CREATE EXTENSION IF NOT EXISTS pgvector;
CREATE TABLE document_embeddings (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
content TEXT,
embedding vector(1536)
);2. Speeding Up Queries with HNSW Indexing
By default, calculating cosine similarity queries requires a full table scan. For large datasets, this is too slow. We speed this up by creating an HNSW (Hierarchical Navigable Small World) index.
CREATE INDEX document_embeddings_hnsw_idx
ON document_embeddings
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);3. Writing Hybrid Search Queries
Combine the semantic power of vector embeddings with classic text search (Full-Text Search) using Reciprocal Rank Fusion (RRF) to deliver highly accurate, contextual search results.